Time-domain analysis of piezoelectric impedance-based structural health monitoring using multilevel wavelet decomposition

Filho, Jozué Vieira; Baptista, Fabrício Guimarães; Inman, Daniel J.

doi:10.1016/j.ymssp.2010.12.003

Cited by 56 publications

(27 citation statements)

References 31 publications

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“…A technique based on the Electromechanical Impedance (EMI) for SHM systems was originally proposed by Liang et al (2014) and has been improved by several other authors ( Annmandas & Soh, 2010;Chaudhry, Lalande, Ganino, Rogers, & Chung, 1996;Cortez, Vieira Filho, & Baptista, 2012;Giurgiutiu & Zagrai, 2002;Sun, Chaudhry, Liang, & Rogers, 1995;Vieira Filho, Baptista, & Inman, 2011;Yang, Liu, Annamdas, & Soh, 2009;Zagrai & Giurgiutiu, 2001 ). This technique is a Non-Destructive Evaluation (NDE) method which is based on the Frequency Response Function (FRF).…”

Section: Time-domain Analysis Based On the Electromechanical Impedancementioning

confidence: 99%

Kappa-PSO-FAN based method for damage identification on composite structural health monitoring

Oliveira

Araújo

Inman

et al. 2018

Expert Systems with Applications

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a b s t r a c tRecently much research has been conducted towards finding fast and accurate pattern classifiers applied to Structural Health Monitoring (SHM) systems. In this way, researchers have proposed new methods based on Fuzzy ARTMAP Network (FAN) in order to enhance the success rate for structural damage classification applied to SHM applications. Conversely, the performance of methods based on FAN is very dependent of its setup parameters. In several SHM approaches in the literature, authors have proposed selecting those parameters by using several attempts (empirical and manual selection) and keeping them fixed for all cases in the resulting analysis, hampering the success rate of the neural network. To overcoming that, this paper introduces a new strategy for enhancement of structural damage identification focusing on supervised learning of FAN by using Particle Swarm Optimization (PSO) for selecting optimal setup parameters automatically for the FAN algorithm. Also, the Kappa coefficient is used as an objective function to be maximized through the PSO algorithm. As a result, the optimum setup parameters improved the success rate while the damage identification is being carried out. Indeed this proposed method is certainly very promising and constitutes a novelty. The proposed method achieves more than 75% hit rate that is significantly higher than the state-of-the-art approaches as presented in this paper. Furthermore, this approach yields a 20% improvement when considering the worst case scenario. Hence, this approach shows a practical application of expert and intelligent systems applied to damage identification in SHM systems. To conclude, the proposed approach successfully identifies structural damage with accuracy and efficiency.

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Section: Time-domain Analysis Based On the Electromechanical Impedancementioning

confidence: 99%

Kappa-PSO-FAN based method for damage identification on composite structural health monitoring

Oliveira

Araújo

Inman

et al. 2018

Expert Systems with Applications

Self Cite

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“…For that, the PZT transducer is excited, and only its time response signal (voltage) is directly used to compute Euclidean Distances. It is important to remark that even analysis of the excitation signal has not shown that the time response signals are correlated with the electromechanical impedance (since it is guaranteed that the excitation input is kept constant) [ 37 , 50 ]. Hence, working on the time domain simplifies and speeds up the damage identification because the EMI is not computed, which by itself, is a great advantage in terms of processing time [ 37 , 50 ].…”

Section: Resultsmentioning

confidence: 99%

Use of Savitzky–Golay Filter for Performances Improvement of SHM Systems Based on Neural Networks and Distributed PZT Sensors

Oliveira¹,

Araújo

Silva³

et al. 2018

Sensors

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A considerable amount of research has focused on monitoring structural damage using Structural Health Monitoring (SHM) technologies, which has had recent advances. However, it is important to note the challenges and unresolved problems that disqualify currently developed monitoring systems. One of the frontline SHM technologies, the Electromechanical Impedance (EMI) technique, has shown its potential to overcome remaining problems and challenges. Unfortunately, the recently developed neural network algorithms have not shown significant improvements in the accuracy of rate and the required processing time. In order to fill this gap in advanced neural networks used with EMI techniques, this paper proposes an enhanced and reliable strategy for improving the structural damage detection via: (1) Savitzky–Golay (SG) filter, using both first and second derivatives; (2) Probabilistic Neural Network (PNN); and, (3) Simplified Fuzzy ARTMAP Network (SFAN). Those three methods were employed to analyze the EMI data experimentally obtained from an aluminum plate containing three attached PZT (Lead Zirconate Titanate) patches. In this present study, the damage scenarios were simulated by attaching a small metallic nut at three different positions in the aluminum plate. We found that the proposed method achieves a hit rate of more than 83%, which is significantly higher than current state-of-the-art approaches. Furthermore, this approach results in an improvement of 93% when considering the best case scenario.

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“…A composite signal with two dominant frequencies was constructed by (6)-(8), as shown in Figure 8, and the parameters were set as 1 =2, 1 =0.5, 2 =5, 2 =0.5, sf =256, N=512, and k=0. 3.…”

Section: Emd Combined With Mfmentioning

confidence: 99%

“…Signal decomposition techniques, such as Fourier transform (FT), wavelet transform (WT), and empirical mode decomposition (EMD), are widely used in the field of structural health monitoring for denoising, parameter identification, and damage detection [1][2][3]. However, these techniques have considerable deficiencies.…”

Section: Introductionmentioning

confidence: 99%

Morphological Filter-Assisted Ensemble Empirical Mode Decomposition

Zhou

Shan

2018

Mathematical Problems in Engineering

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In the ensemble empirical mode decomposition (EEMD) algorithm, different realizations of white noise are added to the original signal as dyadic filter banks to overcome the mode mixing problems of empirical mode decomposition (EMD). However, not all the components in white noise are necessary, and the superfluous components will introduce additional mode mixing problems. To address this problem, morphological filter-assisted ensemble empirical mode decomposition (MF-EEMD) was proposed in this paper. First, a new method for determining the structuring element shape and size was proposed to improve the adaptive ability of morphological filter (MF). Then, the adaptive MF was introduced into EMD to remove the superfluous white noise components to improve the decomposition results. Based on the contributions of MF in a single EMD process, the MF-EEMD was proposed by combining EEMD with MF to suppress the mode mixing problems. Finally, an analog signal and a measured signal were used to verify the feasibility of MF-EEMD. The results show that MF-EEMD significantly mitigates the mode mixing problems and achieves a higher decomposition efficiency compared to that of EEMD.

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Time-domain analysis of piezoelectric impedance-based structural health monitoring using multilevel wavelet decomposition

Cited by 56 publications

References 31 publications

Kappa-PSO-FAN based method for damage identification on composite structural health monitoring

Kappa-PSO-FAN based method for damage identification on composite structural health monitoring

Use of Savitzky–Golay Filter for Performances Improvement of SHM Systems Based on Neural Networks and Distributed PZT Sensors

Morphological Filter-Assisted Ensemble Empirical Mode Decomposition

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